Covid Report for Family

Background

Notes and format last updated May 7, 2020

Starting on the May 7th update, the NY Times began including probable covid cases/deaths along with confirmed. This mostly affects death counts – for certain geographies that include probable COVID deaths in addition to confirmed, these are now added to the totals. For the time being, they were all added to the May 6th totals, causing a big spike at the U.S. level. Over time, NY Times will revise their historical counts and distribute these added deaths when they actually occurred, so the spike should fade.

• This page shows COVID data broken down into regions that affect our family. It pulls from publicly available raw data from the NY Times, which is reported daily. It’s on a one-day lag, so when you see this report, the most recent day included will be that reported the day prior.
• The table of contents above summarizes the major sections and can be used to jump to those sections.
• Each major section is then divided into tabs to allow you to more easily shuffle between geographies and metrics.
  • If you open on your phone, I don’t believe the tabs will work, so you’ll just see the page as one long, continuous document.
• More detailed descriptions and context can be found in each section.

Growth rates

Heat maps

• The two heat maps below compare how quickly total cases or deaths have grown at various times in our respective geopgraphies.
• The first plot compares growth rate for total cases; the second, growth rate for total deaths.
• The metric used is doubling time, by which I mean how quickly total cases or deaths are doubling.
• The plots track that doubling time at each date for our geographies. Darker colors reflect shorter doubling times, and thus periods of faster growth.
  • You can use the plots to track each geography over time and to compare the geographies to one another.
  • You can also compare the cases and death charts, to see how faster periods of death growth follow faster periods of case growth.

Case growth rates

• This section charts the growth rate of both total and new cases for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total cases and new cases.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of cases, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new cases) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new case reporting at the state level.
  • For total cases, the trendlines are a rolling 3-day average of daily growth rates in total cases. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total cases have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total case line get as close to zero as possible.
  • For new cases, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new cases. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total cases, we want to watch for the lines for new cases to get consistently below zero and stay there. That means that we are consistently seeing fewer new cases on a daily basis.

U.S.

Our states

Death growth rates

• This section charts the growth rate of both total and new deaths for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total deaths and new deaths.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of deaths, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new deaths) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new death reporting at the state level.
  • For total deaths, the trendlines are a rolling 3-day average of daily growth rates in total deaths. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total deaths have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total death line get as close to zero as possible.
  • For new deaths, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new deaths. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total deaths, we want to watch for the lines for new deaths to get consistently below zero and stay there. That means that we are consistently seeing fewer new deaths on a daily basis.

U.S.

Our states

By population rankings

This section tracks metrics for states and counties normalized for population (number of cases or deaths per million residents), and then compares these figures both for our geographies and the country overall.

States

• This section shows tables ranking all 50 states for per populations rates of total cases, new cases, total deaths, and new deaths.
• For each metric, in addition to the tables, the trends for the top states are plotted over time.
  • We only plot the top ten states for each metric so that the plots aren’t too crowded. But you can view the full 50-state rankings in the tables.

Total confirmed cases

Table of total confirmed cases per million residents (all 50 states)

Ranking	State	Cases Per Million
1	New York	19,825
2	New Jersey	18,668
3	Massachusetts	15,185
4	Rhode Island	14,973
5	District of Columbia	13,586
6	Connecticut	12,470
7	Illinois	10,395
8	Delaware	10,378
9	Maryland	10,060
10	Louisiana	9,590
11	Nebraska	8,434
12	Iowa	7,322
13	Michigan	6,571
14	South Dakota	6,403
15	Pennsylvania	6,400
16	Mississippi	6,210
17	Virginia	6,167
18	Indiana	5,899
19	Minnesota	5,203
20	Colorado	4,971
21	Georgia	4,928
22	Alabama	4,659
23	New Mexico	4,467
24	Arizona	4,331
25	Tennessee	4,212
26	Utah	4,169
27	North Dakota	3,914
28	New Hampshire	3,830
29	North Carolina	3,788
30	Wisconsin	3,786
31	Kansas	3,770
32	California	3,636
33	Arkansas	3,584
34	Washington	3,436
35	Ohio	3,422
36	Nevada	3,397
37	Florida	3,215
38	South Carolina	3,193
39	Texas	2,889
40	Kentucky	2,714
41	Missouri	2,594
42	Maine	1,984
43	Oklahoma	1,927
44	Idaho	1,848
45	Vermont	1,778
46	Wyoming	1,743
47	Puerto Rico	1,675
48	Oregon	1,244
49	West Virginia	1,237
50	Alaska	910
51	Montana	526
52	Hawaii	480

New confirmed cases

Table of new cases per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Cases Per Million
1	Arizona	172
2	Alabama	130
3	Arkansas	118
4	South Carolina	106
5	Iowa	104
6	Utah	102
7	Louisiana	101
8	North Carolina	100
9	Maryland	99
10	Nebraska	97
11	District of Columbia	94
12	Mississippi	80
13	California	79
14	Tennessee	75
15	Texas	74
16	Georgia	73
17	South Dakota	73
18	Nevada	70
19	Rhode Island	69
20	Minnesota	65
21	Florida	64
22	Illinois	57
23	Indiana	57
24	Virginia	54
25	Massachusetts	50
26	Wisconsin	50
27	New Jersey	49
28	New Mexico	48
29	Delaware	45
30	North Dakota	43
31	Missouri	41
32	Pennsylvania	39
33	New York	35
34	Oklahoma	35
35	Connecticut	34
36	Ohio	33
37	Kentucky	31
38	New Hampshire	31
39	Puerto Rico	31
40	Kansas	29
41	Wyoming	28
42	Alaska	26
43	Colorado	26
44	Oregon	25
45	Washington	25
46	Michigan	23
47	Idaho	19
48	Maine	19
49	Vermont	18
50	West Virginia	10
51	Montana	4
52	Hawaii	3

Total deaths

Table of total deaths per million residents (all 50 states)

Ranking	State	Deaths Per Million
1	New York	1,564
2	New Jersey	1,400
3	Connecticut	1,162
4	Massachusetts	1,086
5	Rhode Island	776
6	District of Columbia	711
7	Louisiana	642
8	Michigan	599
9	Illinois	504
10	Pennsylvania	483
11	Maryland	475
12	Delaware	425
13	Indiana	353
14	Mississippi	291
15	Colorado	274
16	Minnesota	226
17	New Hampshire	226
18	Georgia	220
19	Ohio	213
20	Iowa	203
21	New Mexico	200
22	Virginia	178
23	Washington	157
24	Arizona	155
25	Alabama	153
26	Nevada	149
27	Missouri	142
28	Florida	132
29	California	125
30	Wisconsin	117
31	Kentucky	114
32	South Carolina	114
33	Nebraska	112
34	North Carolina	106
35	North Dakota	101
36	Oklahoma	90
37	Vermont	88
38	Kansas	83
39	South Dakota	82
40	Maine	74
41	Texas	66
42	Tennessee	64
43	Arkansas	56
44	Idaho	48
45	West Virginia	47
46	Puerto Rico	45
47	Oregon	40
48	Utah	40
49	Wyoming	31
50	Montana	16
51	Alaska	12
52	Hawaii	12

New deaths

Table of new deaths per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Deaths Per Million
1	New Jersey	8
2	Rhode Island	7
3	Illinois	6
4	Massachusetts	6
5	Connecticut	5
6	Delaware	5
7	District of Columbia	5
8	Georgia	5
9	Maryland	5
10	New Hampshire	5
11	Minnesota	4
12	Pennsylvania	4
13	Arizona	3
14	Indiana	3
15	Louisiana	3
16	Mississippi	3
17	Nebraska	3
18	New Mexico	3
19	New York	3
20	South Dakota	3
21	Alabama	2
22	California	2
23	Colorado	2
24	Florida	2
25	Michigan	2
26	Missouri	2
27	North Carolina	2
28	Ohio	2
29	South Carolina	2
30	Wisconsin	2
31	Arkansas	1
32	Iowa	1
33	Kentucky	1
34	Nevada	1
35	Tennessee	1
36	Virginia	1
37	Washington	1
38	Alaska	0
39	Hawaii	0
40	Idaho	0
41	Kansas	0
42	Maine	0
43	Montana	0
44	North Dakota	0
45	Oklahoma	0
46	Oregon	0
47	Puerto Rico	0
48	Texas	0
49	Utah	0
50	Vermont	0
51	West Virginia	0
52	Wyoming	0

Counties

• This section focuses on the county level. It shows tables with our counties ranked by percentile of U.S. counties for per population rates of total cases and total deaths.
  • Each table also shows the top five counties in the country in addition to our counties, for added perspecive.
• In addition to the tables, our counties’ percentile for both total cases and total deaths are plotted over time.

Confirmed cases

Table showing total cases per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Cases Per Million	Raw Ranking	Percentile
Trousdale	Tennessee	123,981	1	99
Lake	Tennessee	98,062	2	99
Dakota	Nebraska	86,438	3	99
Lincoln	Arkansas	75,706	4	99
Nobles	Minnesota	74,067	5	99
Davidson	Tennessee	9,547	244	92
Richland	South Carolina	4,825	606	80
Pierce	Washington	2,618	1103	64
Orange	California	2,589	1113	64
York	South Carolina	1,982	1358	56

Our county percentiles over time

Deaths

Table showing total deaths per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Deaths Per Million	Raw Ranking	Percentile
Hancock	Georgia	3,429	1	99
Terrell	Georgia	3,048	2	99
Early	Georgia	3,042	3	99
Randolph	Georgia	2,951	4	99
Northampton	Virginia	2,306	5	99
Richland	South Carolina	168	651	79
Davidson	Tennessee	117	815	74
Pierce	Washington	96	927	70
Orange	California	64	1151	63
York	South Carolina	36	1451	53

Our county percentiles over time

Raw counts

• This section shows the raw counts over time for the U.S., our states, and our counties for four categories:
  • Total confirmed cases: This is the cumulative total of cases reported.
  • New confirmed cases: This is the number of new cases reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new cases, to help better visualize the peak.
  • Total deaths: This is the cumulative total of deaths reported.
  • New deaths: This is the number of new deaths reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new deaths, to help better visualize the peak.

Total confirmed cases

U.S.

Our states

Our counties

New confirmed cases

U.S.

Our states

Our counties

Total deaths

U.S.

Our states

Our counties

New deaths

U.S.

Our states

Our counties

Stay-at-home comparisons

• This section is a work in progress. It’s exploring what effect stay-at-home orders have on spread. It’s inherentely messy because some states have localized orders but not orders for their full state and because states implemented the orders at different times.
  • The first plot shows a rolling 3-day average of new cases per million residents, averaged across 2 groups of states – those with a state-wide stay-at-home order in effect and those without. The size of the dots reflect the number of states in each group for each given day (so the dots are getting bigger over time for the group with orders, as more states implement those orders).
    • So far, this view doesn’t show if these orders are “working” or not. Instead, the biggest takeaway from the current plot is that the states that implemented orders are reacting to having worse spread, which is why the case rate is so much higher at the start for them. As a state decides it has a problem and implements an order, it joins the grouping of states with orders and drives the rate up for that group.
    • Over time (if the policies work), what we’d expect is that the line for the group with orders flattens or declines in relation to the no-order group. It also may become moot if all states issue orders.
    • As states start rescending their orders, I’ll put them back into the no-order group, so the no-order group should beging to expand again.
  • The second plot tracks progress of states by the number of days since orders have been in effect. It includes only those states with orders and shows their average new case growth rate (a rolling 3-day average of daily growth rates) based on how long it’s been since the order went into effect. Again, the size of the dots show how many states are included in each point – i.e., as you go further right, the dots are smaller because less states are that far past their order.
    • I’m working on how to add a baseline to this chart to compare to states without orders, but that’s challenging because there’s no way to fully align timing.